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Reseach Article

Artificial Selection Algorithm

Published on February 2013 by Kunal S Panchal, Jigar K Doshi, Ashish Trivedi
International Conference on Recent Trends in Information Technology and Computer Science 2012
Foundation of Computer Science USA
ICRTITCS2012 - Number 10
February 2013
Authors: Kunal S Panchal, Jigar K Doshi, Ashish Trivedi
6ec3bba5-95fc-40d9-a545-49ee8b5ecfeb

Kunal S Panchal, Jigar K Doshi, Ashish Trivedi . Artificial Selection Algorithm. International Conference on Recent Trends in Information Technology and Computer Science 2012. ICRTITCS2012, 10 (February 2013), 24-28.

@article{
author = { Kunal S Panchal, Jigar K Doshi, Ashish Trivedi },
title = { Artificial Selection Algorithm },
journal = { International Conference on Recent Trends in Information Technology and Computer Science 2012 },
issue_date = { February 2013 },
volume = { ICRTITCS2012 },
number = { 10 },
month = { February },
year = { 2013 },
issn = 0975-8887,
pages = { 24-28 },
numpages = 5,
url = { /proceedings/icrtitcs2012/number10/10319-1464/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Recent Trends in Information Technology and Computer Science 2012
%A Kunal S Panchal
%A Jigar K Doshi
%A Ashish Trivedi
%T Artificial Selection Algorithm
%J International Conference on Recent Trends in Information Technology and Computer Science 2012
%@ 0975-8887
%V ICRTITCS2012
%N 10
%P 24-28
%D 2013
%I International Journal of Computer Applications
Abstract

In a genetic algorithm, a population of strings (called chromosomes), which encode candidate solutions (called individuals or creatures) to an optimization problem, is evolved toward better solutions. Better solution is usually attained in form of succeeding generations. These generations are evolved using techniques inspired from the real world viz. , selections, mutations and crossovers. Till date, selections were performed using the most basic technique called natural selection, in which, the fitter population has a better chance of getting selected to become the parents of the successive generations. This 'natural selection' can be directly mapped to naturalist Charles Darwin's theory of survival of the fittest. However, Darwin also mentioned another method of nature's selection process called the 'Artificial Selection'. This paper is indented towards mapping the methodology of artificial selection in Artificial Intelligence to develop a new algorithm that aims at the same goal of survival. In this method, only the required traits or combination of traits of the population will be carried forward. This is done by introducing a factor which decides which traits are needed and which are not. This factor can be implicitly included in the program or can be explicitly decided by the user input. This method differs from the existing natural selection in the way that it does not depend totally on fitness of the generations but seeks mainly the required traits or combination of traits needed to solve the problem, and also that the generations evolve according to the factor which can sometimes be user itself who decides which traits he wants to be prominent and which are to be discarded. As a result, the successive generations are a product of selective breeding by the factor. Also, the generations can completely be different in terms of traits of their parents.

References
  1. Carl Sagan. COSMOS, Ballantine Books, 1985
  2. David E Goldberg, Genetic Algorithms in search, Optimization & Machine Learning, Addison Wesley.
  3. AISWeb, The Online Home of Artificial Immune Systems, http://www. artificial-immune-systems. org/algorithms. shtml
  4. Potts, J. C. , The development and evaluation of an improved genetic algorithm based on migration and artificial selection, IEEE Transactions on Systems, Man and Cybernetics, Vol 24, Issue 1, 1994, pp 73-86.
  5. Leandro Nunes de Castro, Fernando J. Von Zuben, The Clonal Selection Algorithm with Engineering Applications, In Workshop Proceedings of GECCO, pp. 36-37, Workshop on Artificial Immune Systems and Their Applications, Las Vegas, USA, July 2000.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Selection Algorithm Required Traits Lesser Dependency On Fitness Evolution Controlled By A Factor Selective Breeding